Multi angular remote sensing supplies reflectance of land surface in different directions. To simulate the relationship between bi directional reflectance distribution （ BRDF ） and structure of land surface and optical characteristic of objects

more models were developed. Because they are simple

rapid

grasp the main factors affecting BRDF and have some physical meaning

semi empirical models

especially

kernel driven models are applied broadly in data processing in batches. Thus

kernel driven BRDF model was the core of the AMBRALS

an algorithm for MODIS land surface BRDF and albedo products. In the onboard version of AMBRALS

the LiSparseR Gometrical Optical （GO） kernel was used. But a new derived kernel LiTransit kernel is also good at transition from LiSparse kernel to LiDense kernel when zenith angle is large

and accords more to the basic principle of GO model than LiSparseR kernel. Results of validation show:RossThick LiTransit kernels combination has more stability when extrapolated to large zenith angles with LiSparseR kernel. Therefore

we will use the Litransit kernel instead of LiSparseR kernel in the new version of AMBRALS. We introduce the algorithm based on this new kernel in this paper

including the kernel driven model and its inversion

albedo retrieval based on BRDF model

broad band albedo retrieval and realization of this algorithm. The speed requirement of tremendous data processing can’t be met easily

such as MODIS data. Although we can calculate the integration of the kernel beforehand

store up and acquire through look up table method during retrieving albedo

it’s inconvenient for an integrated data processing system. Thus we need to get the simple form of the integration of the kernels. Because the integrations of the kernels are approxinately independent on directions than BRDF

it’s sufficient to use a polynome dependent on the solar zenith angle to regress the integration of kernel. In this paper

we study the polynome regression of LiTransit kernel to instead LiSparseR

but not affect the systematic of the algorithm at the same time. Comparing the numerical integration of the kernel and the polynome regression result show the relationship is very well

the polynome can be used in the algorithm directly.